Corrigendum to "Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?" [NeuroImage 127 (2016) 242-256]

Comparision of spontaneous brain activity between hippocampal sclerosis and MRI-negative temporal lobe epilepsy

BACKGROUND

Hippocampal sclerosis (HS) is a prevalent cause of temporal lobe epilepsy (TLE). However, up to 30% of individuals with TLE present negative magnetic resonance imaging (MRI) findings. A comprehensive grasp of the similarities and differences in brain activity among distinct TLE subtypes holds significant clinical and scientific importance.

OBJECTIVE

To comprehensively examine the similarities and differences between TLE with HS (TLE-HS) and MRI-negative TLE (TLE-N) regarding static and dynamic abnormalities in spontaneous brain activity (SBA). Furthermore, we aimed to determine whether these alterations correlate with epilepsy duration and cognition, and to determine a potential differential diagnostic index for clinical utility.

METHODS

We measured 12 SBA metrics in 38 patients with TLE-HS, 51 with TLE-N, and 53 healthy volunteers. Voxel-wise analysis of variance (ANOVA) and post-hoc comparisons were employed to compare these metrics. The six static metrics included amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), degree centrality (DC), and global signal correlation (GSCorr). Additionally, six corresponding dynamic metrics were assessed: dynamic ALFF (dALFF), dynamic fALFF (dfALFF), dynamic ReHo (dReHo), dynamic DC (dDC), dynamic VMHC (dVMHC), and dynamic GSCorr (dGSCorr). Receiver operating characteristic (ROC) curve analysis of abnormal indices was employed. Spearman correlation analyses were also conducted to examine the relationship between the abnormal indices, epilepsy duration and cognition scores.

RESULTS

Both TLE-HS and TLE-N presented as extensive neural network disorders, sharing similar patterns of SBA alterations. The regions with increased fALFF, dALFF, and dfALFF levels were predominantly observed in the mesial temporal lobe, thalamus, basal ganglia, pons, and cerebellum, forming a previously proposed mesial temporal epilepsy network. Conversely, decreased SBA metrics (fALFF, ReHo, dReHo, DC, GSCorr, and VMHC) consistently appeared in the lateral temporal lobe ipsilateral to the epileptic foci. Notably, SBA alterations were more obvious in patients with TLE-HS than in those with TLE-N. Additionally, patients with TLE-HS exhibited reduced VMHC in both mesial and lateral temporal lobes compared with patients with TLE-N, with the hippocampus displaying moderate discriminatory power (AUC = 0.759). Correlation analysis suggested that alterations in SBA indicators may be associated with epilepsy duration and cognitive scores.

CONCLUSIONS

The simultaneous use of static and dynamic SBA metrics provides evidence supporting the characterisation of both TLE-HS and TLE-N as complex network diseases, facilitating the exploration of mechanisms underlying epileptic activity and cognitive impairment. Overall, SBA abnormality patterns were generally similar between the TLE-HS and TLE-N groups, encompassing networks related to TLE and auditory and occipital visual functions. These changes were more pronounced in the TLE-HS group, particularly within the mesial and lateral temporal lobes.

Similarities and differences of dynamic and static spontaneous brain activity between left and right temporal lobe epilepsy

To comprehensively investigate the potential temporal dynamic and static abnormalities of spontaneous brain activity (SBA) in left temporal lobe epilepsy (LTLE) and right temporal lobe epilepsy (RTLE) and to detect whether these alterations correlate with cognition. Twelve SBA metrics, including ALFF, dALFF, fALFF, dfALFF, ReHo, dReHo, DC, dDC, GSCorr, dGSCorr, VMHC, and dVMHC, in 46 LTLE patients, 43 RTLE patients, and 53 healthy volunteers were compared in the voxel-wise analysis. Correlation analyses between metrics in regions showing statistic differences and epilepsy duration, epilepsy severity, and cognition scores were also performed. Compared with the healthy volunteers, the alteration of SBA was identified both in LTLE and RTLE patients. The ALFF, fALFF, and dALFF values in LTLE, as well as the fALFF values in RTLE, increased in the bilateral thalamus, basal ganglia, mesial temporal lobe, cerebellum, and vermis. Increased dfALFF in the bilateral basal ganglia, increased ReHo and dReHo in the bilateral thalamus in the LTLE group, increased ALFF and dALFF in the pons, and increased ReHo and dReHo in the right hippocampus in the RTLE group were also detected. However, the majority of deactivation clusters were in the ipsilateral lateral temporal lobe. For LTLE, the fALFF, DC, dDC, and GSCorr values in the left lateral temporal lobe and the ReHo and VMHC values in the bilateral lateral temporal lobe all decreased. For RTLE, the ALFF, fALFF, dfALFF, ReHo, dReHo, and DC values in the right lateral temporal lobe and the VMHC values in the bilateral lateral temporal lobe all decreased. Moreover, for both the LTLE and RTLE groups, the dVMHC values decreased in the calcarine cortex. The most significant difference between LTLE and RTLE was the higher activation in the cerebellum of the LTLE group. The alterations of many SBA metrics were correlated with cognition and epilepsy duration. The patterns of change in SBA abnormalities in the LTLE and RTLE patients were generally similar. The integrated application of temporal dynamic and static SBA metrics might aid in the investigation of the propagation and suppression pathways of seizure activity as well as the cognitive impairment mechanisms in TLE.

Classification of Major Depressive Disorder Based on Integrated Temporal and Spatial Functional MRI Variability Features of Dynamic Brain Network

BACKGROUND

Characterization of the dynamics of functional brain network has gained increased attention in the study of depression. However, most studies have focused on single temporal dimension, while ignoring spatial dimensional information, hampering the discovery of validated biomarkers for depression.

PURPOSE

To integrate temporal and spatial functional MRI variability features of dynamic brain network in machine-learning techniques to distinguish patients with major depressive disorder (MDD) from healthy controls (HCs).

STUDY TYPE

Prospective.

POPULATION

A discovery cohort including 119 patients and 106 HCs and an external validation cohort including 126 patients and 124 HCs from Rest-meta-MDD consortium.

FIELD STRENGTH/SEQUENCE

A 3.0 T/resting-state functional MRI using the gradient echo sequence.

ASSESSMENT

A random forest (RF) model integrating temporal and spatial variability features of dynamic brain networks with separate feature selection method (MSFS ) was implemented for MDD classification. Its performance was compared with three RF models that used: temporal variability features (MTVF ), spatial variability features (MSVF ), and integrated temporal and spatial variability features with hybrid feature selection method (MHFS ). A linear regression model based on MSFS was further established to assess MDD symptom severity, with prediction performance evaluated by the correlations between true and predicted scores.

STATISTICAL TESTS

Receiver operating characteristic analyses with the area under the curve (AUC) were used to evaluate models' performance. Pearson's correlation was used to assess relationship of predicted scores and true scores. P < 0.05 was considered statistically significant.

RESULTS

The model with MSFS achieved the best performance, with AUCs of 0.946 and 0.834 in the discovery and validation cohort, respectively. Additionally, altered temporal and spatial variability could significantly predict the severity of depression (r = 0.640) and anxiety (r = 0.616) in MDD.

DATA CONCLUSION

Integration of temporal and spatial variability features provides potential assistance for clinical diagnosis and symptom prediction of MDD.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

Static and Temporal Dynamic Alteration of Intrinsic Brain Activity in MRI-negative Temporal Lobe Epilepsy

PURPOSE

To comprehensively explore the potential brain activity abnormalities affected by MRI-negative temporal lobe epilepsy (TLE) and to detect whether the changes were associated with cognition and help in the diagnosis or lateralization.

METHOD

Six static intrinsic brain activity (IBA) indicators (ALFF, fALFF, ReHo, DC, GSCorr, VMHC) and their corresponding six temporal dynamic indicators in 39 unilateral MRI-negative TLE patients and 42 healthy volunteers were compared. Correlation analyses were performed between these indicators in areas displaying group differences, cognitive function, and epilepsy duration. ROC analyses were performed to test the diagnostic and lateralization ability of the IBA parameters.

RESULTS

Considerable overlap was present among the abnormal brain regions detected by different static and dynamic indicators, including (1) alteration of fALFF, Reho, DC, VMHC, dfALFF, dReHo, and dDC in the temporal neocortex (predominately ipsilateral to the epileptogenic foci); (2) decreased dGSCorr and dVMHC in the occipital lobe. Meanwhile, the ReHo and VMHC values in the temporal neocortex correlated with the cognition scores or epilepsy duration (P < 0.01). The ROC analysis results revealed moderate diagnosis or lateralization efficiency of several IBA indicators (fALFF, dfALFF, ReHo, DC, dDC, and VMHC).

CONCLUSION

The abnormal condition of neuronal activity in the temporal neocortex, predominately lateralized to the epileptic side, was a crucial feature in patients with MRI-negative TLE and might offer diagnosis or lateralization information. The application of dynamic intrinsic brain activity indicators played a complementary role, further revealing the temporal variability decline of the occipital lobe in MRI-negative TLE patients.

Asthma's effect on brain connectivity and cognitive decline

Objective

To investigate the changes in dynamic voxel mirror homotopy connection (dVMHC) between cerebral hemispheres in patients with asthma.

Methods

Our study was designed using a case-control method. A total of 31 subjects with BA and 31 healthy subjects with matching basic information were examined using rsfMRI. We also calculated and obtained the dVMHC value between the cerebral cortexes.

Results

Compared with the normal control group, the dVMHC of the lingual gyrus (Ling) and the calcarine sulcus (CAL), which represented the visual network (VN), increased significantly in the asthma group, while the dVMHC of the medial superior frontal gyrus (MSFG), the anterior/middle/posterior cingulate gyrus (A/M/PCG), and the supplementary motor area (SMA) of the sensorimotor network decreased significantly in the asthma group.

Conclusion

This study showed that the ability of emotion regulation and the efficiency of visual and cognitive information processing in patients with BA was lower than in those in the HC group. The dVMHC analysis can be used to sensitively evaluate oxygen saturation, visual function changes, and attention bias caused by emotional disorders in patients with asthma, as well as to predict airway hyperresponsiveness, inflammatory progression, and dyspnea.

More than just statics: Static and temporal dynamic changes in intrinsic brain activity in unilateral temporal lobe epilepsy

Background

Temporal lobe epilepsy (TLE) is the most prevalent refractory focal epilepsy and is more likely accompanied by cognitive impairment. The fully understanding of the neuronal activity underlying TLE is of great significance.

Objective

This study aimed to comprehensively explore the potential brain activity abnormalities affected by TLE and detect whether the changes were associated with cognition.

Methods

Six static intrinsic brain activity (IBA) indicators [amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree centrality (DC), global signal correlation (GSCorr), and voxel-mirrored homotopic connectivity (VMHC)] and their corresponding dynamic indicators, such as dynamic ALFF (dALFF), dynamic fALFF (dfALFF), dynamic ReHo (dReHo), dynamic DC (dDC), dynamic VMHC (dVMHC), and dynamic GSCorr (dGSCorr), in 57 patients with unilateral TLE and 42 healthy volunteers were compared. Correlation analyses were also performed between these indicators in areas displaying group differences and cognitive function, epilepsy duration, and severity.

Results

Marked overlap was present among the abnormal brain regions detected using various static and dynamic indicators, primarily including increased ALFF/dALFF/fALFF in the bilateral medial temporal lobe and thalamus, decreased ALFF/dALFF/fALFF in the frontal lobe contralateral to the epileptogenic side, decreased fALFF, ReHo, dReHo, DC, dDC, GSCorr, dGSCorr, and VMHC in the temporal neocortex ipsilateral to the epileptogenic foci, decreased dReHo, dDC, dGSCorr, and dVMHC in the occipital lobe, and increased ALFF, fALFF, dfALFF, ReHo, and DC in the supplementary motor area ipsilateral to the epileptogenic foci. Furthermore, most IBA indicators in the abnormal brain region significantly correlated with the duration of epilepsy and several cognitive scale scores (P < 0.05).

Conclusion

The combined application of static and dynamic IBA indicators could comprehensively reveal more real abnormal neuronal activity and the impairment and compensatory mechanisms of cognitive function in TLE. Moreover, it might help in the lateralization of epileptogenic foci and exploration of the transmission and inhibition pathways of epileptic activity.

Altered static and dynamic voxel-mirrored homotopic connectivity in subacute stroke patients: a resting-state fMRI study

Sixty-four subacute stroke patients and 55 age-matched healthy controls (HCs) underwent a resting-state functional magnetic resonance imaging scan using an echo-planar imaging sequence and high-resolution sagittal T1-weighted images using a three-dimensional magnetization-prepared rapid gradient echo sequence. Static and dynamic voxel-mirrored homotopic connectivity (VMHC) was computed, respectively. The relationships between the clinical measures, including National Institutes of Health Stroke Scale (NIHSS), illness duration, Fugl-Meyer assessment for upper and lower extremities (FMA-total) and size of lesion volume, and the static/ dynamic VMHC variability alterations in stroke patients were calculated. The stroke patients showed significantly increased static VMHC in the corpus callosum, middle occipital gyrus and inferior parietal gyrus, and decreased static VMHC in the inferior temporal gyrus and precentral gyrus (PreCG) compared with those of HCs. For dynamic VMHC variability, increased dynamic VMHC variability in the inferior temporal gyrus and PreCG was detected in stroke patients relative to that in HCs. Correlation analysis exhibited that significant negative correlations were shown between the FMA scores and dynamic VMHC variability in PreCG. The present study suggests that combined static and dynamic VMHC could be helpful to evaluate the motor function of stroke patients and understand the intrinsic differences of inter-hemispheric coordination after stroke.

A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction

Increasing evidence has suggested that the dynamic properties of functional brain networks are related to individual behaviors and cognition traits. However, current fMRI-based approaches mostly focus on statistical characteristics of the windowed correlation time course, potentially overlooking subtle time-varying patterns in dynamic functional connectivity (dFC). Here, we proposed the use of an end-to-end deep learning model that combines the convolutional neural network (CNN) and long short-term memory (LSTM) network to capture temporal and spatial features of functional connectivity sequences simultaneously. The results on a large cohort (Human Connectome Project, n = 1,050) demonstrated that our model could achieve a high classification accuracy of about 93% in a gender classification task and prediction accuracies of 0.31 and 0.49 (Pearson's correlation coefficient) in fluid and crystallized intelligence prediction tasks, significantly outperforming previously reported models. Furthermore, we demonstrated that our model could effectively learn spatiotemporal dynamics underlying dFC with high statistical significance based on the null hypothesis estimated using surrogate data. Overall, this study suggests the advantages of a deep learning model in making full use of dynamic information in resting-state functional connectivity, and highlights the potential of time-varying connectivity patterns in improving the prediction of individualized characterization of demographics and cognition traits.

Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia

Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactions – such as their multifractality or information content –, that otherwise remain hidden from conventional static methods. Several neuropsychiatric disorders were shown to be associated with altered DFC, with schizophrenia (SZ) being one of the most intensely studied among such conditions. Here we analyzed resting-state electroencephalography recordings of 14 SZ patients and 14 age- and gender-matched healthy controls (HC). We reconstructed dynamic functional networks from delta band (0.5–4 Hz) neural activity and captured their spatiotemporal dynamics in various global network topological measures. The acquired network measure time series were made subject to dynamic analyses including multifractal analysis and entropy estimation. Besides group-level comparisons, we built a classifier to explore the potential of DFC features in classifying individual cases. We found stronger delta-band connectivity, as well as increased variance of DFC in SZ patients. Surrogate data testing verified the true multifractal nature of DFC in SZ, with patients expressing stronger long-range autocorrelation and degree of multifractality when compared to controls. Entropy analysis indicated reduced temporal complexity of DFC in SZ. When using these indices as features, an overall cross-validation accuracy surpassing 89% could be achieved in classifying individual cases. Our results imply that dynamic features of DFC such as its multifractal properties and entropy are potent markers of altered neural dynamics in SZ and carry significant potential not only in better understanding its pathophysiology but also in improving its diagnosis. The proposed framework is readily applicable for neuropsychiatric disorders other than schizophrenia.

Dynamic Neuroimaging Biomarkers of Smoking in Young Smokers

OBJECTIVE

To examine potential changes in the dynamic characteristics of regional neural activity in young smokers and to detect whether the changes were associated with smoking behavior.

METHODS

The dynamic regional homogeneity (dReHo) and dynamic amplitude of low-frequency fluctuations (dALFF) in 40 young smokers and 42 nonsmokers were compared. Correlation analyses were also performed between dReHo and dALFF in areas showing group differences and smoking behavior [e.g., the Fagerström Test for Nicotine dependence (FTND) scores and pack-years].

RESULTS

Significantly differences in dReHo variability were observed in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), medial frontal gyrus (MFG), insula, cuneus, postcentral gyrus, inferior semi-lunar lobule, orbitofrontal gyrus, and inferior temporal gyrus (ITG). Young smokers also showed significantly increased dALFF variability in the anterior cingulate cortex (ACC) and ITG. Furthermore, a significant positive correlation was found between dALFF variability in the ACC and the pack-years; whereas a significant negative correlation between dReHo variability in the IFG and the FTND scores was found in young smokers.

CONCLUSION

The pattern of resting state regional neural activity variability was different between young smokers and nonsmokers. Dynamic regional indexes might be a novel neuroimaging biomarker of smoking behavior in young smokers.

Depression Affects Intrinsic Brain Activity in Patients With Mild Cognitive Impairment

Numerous observational studies have shown that depressive symptoms are common in individuals with mild cognitive impairment (MCI) who have a higher rate of progress to dementia. However, it is still uncertain whether there are any differences between MCI patients with and without depression symptom in their brain function activities. Here we have identified the brain function activity differences in two groups of MCI patients (with depression or without depression) using the resting state MRI (rsfMRI) measurements. 76 right-handed MCI subjects have been recruited in this study, including 27 MCI patients with depression symptom (MCID), 49 MCI patients without depression symptom (MCIND). Analyses based on 7 rsfMRI measurements, including four static measurements (ALFF, fALFF, PerAF, and ReHo) and three dynamic measurements (dALFF, dfALFF, and dReHo) have been used to explore the temporal variability of intrinsic brain activity. No significant differences in ALFF and dALFF between the two group were found. In the MCID group, fALFF decreased in temporal gyrus, frontal gyrus, inferior occipital gyrus, middle frontal gyrus and cerebellum, but increased in cuneus, calcarine, lingual; while PerAF increased in left parahippocampus. The differences of ReHo in the two groups was only found in cerebellum. Compared to MCIND group, dfALFF in MCID decreased in cuneus, occipital gyrus and calcarine, while dReHo in MCID increased in bilateral temporal gyrus, frontal gyrus, superior parietal gyrus, inferior parietal gyrus and precuneus. Our results may provide a better understanding in the relationship between the depressive symptoms and memory deficits.

Time-resolved connectome of the five-factor model of personality

The human brain is characterized by highly dynamic patterns of functional connectivity. However, it is unknown whether this time-variant 'connectome' is related to the individual differences in the behavioural and cognitive traits described in the five-factor model of personality. To answer this question, inter-network time-variant connectivity was computed in n = 818 healthy people via a dynamical conditional correlation model. Next, network dynamicity was quantified throughout an ad-hoc measure (T-index) and the generalizability of the multi-variate associations between personality traits and network dynamicity was assessed using a train/test split approach. Conscientiousness, reflecting enhanced cognitive and emotional control, was the sole trait linked to stationary connectivity across several circuits such as the default mode and prefronto-parietal network. The stationarity in the 'communication' across large-scale networks offers a mechanistic description of the capacity of conscientious people to 'protect' non-immediate goals against interference over-time. This study informs future research aiming at developing more realistic models of the brain dynamics mediating personality differences.

Hub Patterns-Based Detection of Dynamic Functional Network Metastates in Resting State: A Test-Retest Analysis

The spontaneous dynamic characteristics of resting-state functional networks contain much internal brain physiological or pathological information. The metastate analysis of brain functional networks is an effective technique to quantify the essence of brain functional connectome dynamics. However, the widely used functional connectivity-based metastate analysis ignored the topological structure, which could be locally reflected by node centrality. In this study, 23 healthy young volunteers (21-26 years) were recruited and scanned twice with a 1-week interval. Based on the time sequences of node centrality, we promoted a node centrality-based clustering method to find metastates of functional connectome and conducted a test-retest experiment to assess the stability of those identified metastates using the described method. The hub regions of metastates were further compared with the structural networks' organization to depict its potential relationship with brain structure. Results of extracted metastates showed repeatable dynamic features between repeated scans and high overlapping rate of hub regions with brain intrinsic sub-networks. These identified hub patterns from metastates further highly overlapped with the structural hub regions. These findings indicated that the proposed node centrality-based metastates detection method could reveal reliable and meaningful metastates of spontaneous dynamics and indicate the underlying nature of brain dynamics as well as the potential relationship between these dynamics and the organization of the brain connectome.

Reduction in gray matter of cerebellum in schizophrenia and its influence on static and dynamic connectivity

Pathophysiological and atrophic changes in the cerebellum have been well-documented in schizophrenia. Reduction of gray matter (GM) in the cerebellum was confirmed across cognitive and motor cerebellar modules in schizophrenia. Such abnormalities in the cerebellum could potentially have widespread effects on both sensorimotor and cognitive symptoms. In this study, we investigated how reduction change in the cerebellum affects the static and the dynamic functional connectivity (FC) between the cerebellum and cortical/subcortical networks in schizophrenia. Reduction of GM in the cerebellum was confirmed across the cognitive and motor cerebellar modules in schizophrenic subjects. Results from this study demonstrates that the extent of reduction of GM within cerebellum correlated with increased static FCs between the cerebellum and the cortical/subcortical networks, including frontoparietal network (FPN), and thalamus in patients with schizophrenia. Decreased GM in the cerebellum was also associated with a declined dynamic FC between the cerebellum and the FPN in schizophrenic subjects. The severity of patients' positive symptom was related to these structural-functional coupling score of cerebellum. These findings identified potential cerebellar driven functional changes associated with positive symptom deficits. A post hoc analysis exploring the effect of changed FC within cerebellum, confirmed that a significant positive relationship, between dynamic FCs of cerebellum-thalamus and intracerebellum existed in patients, but not in controls. The reduction of GM within the cerebellum might be associated with modulation of cerebellum-thalamus, and contributes to the dysfunctional cerebellar-cortical communication in schizophrenia. Our results provide a new insight into the role of cerebellum in understanding the pathophysiological of schizophrenia.

Dynamic Alterations in Spontaneous Neural Activity in Multiple Brain Networks in Subacute Stroke Patients: A Resting-State fMRI Study

Objective: To examine whether subacute stroke patients would exhibit abnormal dynamic characteristics of brain activity relative to healthy controls (HC) and to investigate whether the altered dynamic regional indexes were associated with clinical behavior in stroke patients. Methods: The dynamic amplitude of low-frequency fluctuations (dALFF) and dynamic regional homogeneity (dReHo) in 42 subacute stroke patients and 55 healthy controls were compared. Correlation analyses between dALFF and dReHo in regions showing significant intergroup differences and clinical scores (i.e., the National Institutes of Health Stroke Scale, Fugl-Meyer assessment and lesion volume size) were conducted in stroke patients. Receiver operating characteristic (ROC) curve analysis was used to determine the potential value of altered dynamic regional indexes to identify stroke patients. Results: Significantly dALFF in the bilateral cerebellum posterior lobe (CPL), ipsilesional superior parietal lobe, ipsilesional inferior temporal gyrus (ITG), the midline supplementary motor area (SMA), ipsilesional putamen and lentiform nucleus were detected in stroke patients compared to HC. Relative to the HC group, the stroke patients showed significant differences in dReHo in the contralesional rectal gyrus, contralesional ITG, contralesional pons, ipsilesional middle frontal gyrus (MFG). Significant correlations between dALFF variability in midline SMA and Fugl-Meyer assessment (FMA) scores or between dReHo variability in the ipsilesional MFG and FMA scores were detected in stroke patients. Furthermore, the ROC curve revealed that dynamic ALFF at SMA and ReHo at ipsilesional MFG might have the potential to distinguish stroke patients. Conclusion: The pattern of intrinsic brain activity variability is altered in stroke patients compared with HC, and dynamic ALFF/ReHo might be potential tools to assess stroke patients' motor function.

The effects of serotonin modulation on medial prefrontal connectivity strength and stability: A pharmacological fMRI study with citalopram

BACKGROUND

Static and dynamic functional connectivity are being increasingly used to measure the effects of disease and a range of different interventions on brain networks. While preliminary evidence suggests that static connectivity can be modulated by chronic antidepressants administration in healthy individuals and in major depression, much less is known about the acute effects of antidepressants especially on dynamic functional connectivity changes. Here we examine acute effects of antidepressants on dynamic functional connectivity within the default mode network. The default mode network is a well described network with many functions in which the role of serotonin is not clear.

METHODS

In this work we measured acute pharmacological effects of an infusion of the selective serotonin reuptake inhibitor (SSRI) citalopram (10 mg) in a sample of thirteen healthy volunteers randomised to receive on two occasions the active compound or placebo in a cross over dosing.

RESULTS

Acute citalopram administration relative to placebo increased static connectivity between the medial prefrontal cortex and right dorsolateral prefrontal cortex and posterior cingulate cortex. The SSRI also induced a reduction in variability of connectivity with the medial prefrontal cortex in the precuneus and posterior cingulate cortex.

DISCUSSION

The measured changes are compatible with modified serotonin cortical availability.

Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data

Functional MR imaging (fMR imaging) studies have recently begun to examine spontaneous changes in interregional interactions (functional connectivity) over seconds to minutes, and their relation to natural shifts in cognitive and physiologic states. This practice opens the potential for uncovering structured, transient configurations of coordinated brain activity whose features may provide novel cognitive and clinical biomarkers. However, analysis of these time-varying phenomena requires careful differentiation between neural and nonneural contributions to the fMR imaging signal and thorough validation and statistical testing. In this article, the authors present an overview of methodological and interpretational considerations in this emerging field.

Analytical Operations Relate Structural and Functional Connectivity in the Brain

Resting-state large-scale brain models vary in the amount of biological elements they incorporate and in the way they are being tested. One might expect that the more realistic the model is, the closer it should reproduce real functional data. It has been shown, instead, that when linear correlation across long BOLD fMRI time-series is used as a measure for functional connectivity (FC) to compare simulated and real data, a simple model performs just as well, or even better, than more sophisticated ones. The model in question is a simple linear model, which considers the physiological noise that is pervasively present in our brain while it diffuses across the white-matter connections, that is structural connectivity (SC). We deeply investigate this linear model, providing an analytical solution to straightforwardly compute FC from SC without the need of computationally costly simulations of time-series. We provide a few examples how this analytical solution could be used to perform a fast and detailed parameter exploration or to investigate resting-state non-stationarities. Most importantly, by inverting the analytical solution, we propose a method to retrieve information on the anatomical structure directly from functional data. This simple method can be used to complement or guide DTI/DSI and tractography results, especially for a better assessment of inter-hemispheric connections, or to provide an estimate of SC when only functional data are available.